Overview

Dataset statistics

Number of variables19
Number of observations1600
Missing cells386
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory237.6 KiB
Average record size in memory152.1 B

Variable types

Numeric14
Categorical5

Alerts

First Name has a high cardinality: 1215 distinct valuesHigh cardinality
Last Name has a high cardinality: 1378 distinct valuesHigh cardinality
Birthday has a high cardinality: 1065 distinct valuesHigh cardinality
Astronomy is highly overall correlated with Defense Against the Dark Arts and 1 other fieldsHigh correlation
Herbology is highly overall correlated with Charms and 1 other fieldsHigh correlation
Defense Against the Dark Arts is highly overall correlated with Astronomy and 1 other fieldsHigh correlation
Divination is highly overall correlated with Hogwarts HouseHigh correlation
Muggle Studies is highly overall correlated with Charms and 1 other fieldsHigh correlation
Ancient Runes is highly overall correlated with Flying and 1 other fieldsHigh correlation
History of Magic is highly overall correlated with Potions and 3 other fieldsHigh correlation
Transfiguration is highly overall correlated with Potions and 2 other fieldsHigh correlation
Potions is highly overall correlated with History of Magic and 2 other fieldsHigh correlation
Charms is highly overall correlated with Herbology and 3 other fieldsHigh correlation
Flying is highly overall correlated with Ancient Runes and 4 other fieldsHigh correlation
Hogwarts House is highly overall correlated with Astronomy and 9 other fieldsHigh correlation
Arithmancy has 34 (2.1%) missing valuesMissing
Astronomy has 32 (2.0%) missing valuesMissing
Herbology has 33 (2.1%) missing valuesMissing
Defense Against the Dark Arts has 31 (1.9%) missing valuesMissing
Divination has 39 (2.4%) missing valuesMissing
Muggle Studies has 35 (2.2%) missing valuesMissing
Ancient Runes has 35 (2.2%) missing valuesMissing
History of Magic has 43 (2.7%) missing valuesMissing
Transfiguration has 34 (2.1%) missing valuesMissing
Potions has 30 (1.9%) missing valuesMissing
Care of Magical Creatures has 40 (2.5%) missing valuesMissing
Index is uniformly distributedUniform
First Name is uniformly distributedUniform
Last Name is uniformly distributedUniform
Birthday is uniformly distributedUniform
Index has unique valuesUnique
Charms has unique valuesUnique

Reproduction

Analysis started2023-01-27 15:58:16.410938
Analysis finished2023-01-27 15:58:32.983040
Duration16.57 seconds
Software versionpandas-profiling vv3.6.3
Download configurationconfig.json

Variables

Index
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct1600
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean799.5
Minimum0
Maximum1599
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-01-27T16:58:33.064119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile79.95
Q1399.75
median799.5
Q31199.25
95-th percentile1519.05
Maximum1599
Range1599
Interquartile range (IQR)799.5

Descriptive statistics

Standard deviation462.02453
Coefficient of variation (CV)0.57789185
Kurtosis-1.2
Mean799.5
Median Absolute Deviation (MAD)400
Skewness0
Sum1279200
Variance213466.67
MonotonicityStrictly increasing
2023-01-27T16:58:33.153454image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.1%
1063 1
 
0.1%
1073 1
 
0.1%
1072 1
 
0.1%
1071 1
 
0.1%
1070 1
 
0.1%
1069 1
 
0.1%
1068 1
 
0.1%
1067 1
 
0.1%
1066 1
 
0.1%
Other values (1590) 1590
99.4%
ValueCountFrequency (%)
0 1
0.1%
1 1
0.1%
2 1
0.1%
3 1
0.1%
4 1
0.1%
5 1
0.1%
6 1
0.1%
7 1
0.1%
8 1
0.1%
9 1
0.1%
ValueCountFrequency (%)
1599 1
0.1%
1598 1
0.1%
1597 1
0.1%
1596 1
0.1%
1595 1
0.1%
1594 1
0.1%
1593 1
0.1%
1592 1
0.1%
1591 1
0.1%
1590 1
0.1%

Hogwarts House
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size12.6 KiB
Hufflepuff
529 
Ravenclaw
443 
Gryffindor
327 
Slytherin
301 

Length

Max length10
Median length10
Mean length9.535
Min length9

Characters and Unicode

Total characters15256
Distinct characters21
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowRavenclaw
2nd rowSlytherin
3rd rowRavenclaw
4th rowGryffindor
5th rowGryffindor

Common Values

ValueCountFrequency (%)
Hufflepuff 529
33.1%
Ravenclaw 443
27.7%
Gryffindor 327
20.4%
Slytherin 301
18.8%

Length

2023-01-27T16:58:33.220376image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T16:58:33.295154image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
hufflepuff 529
33.1%
ravenclaw 443
27.7%
gryffindor 327
20.4%
slytherin 301
18.8%

Most occurring characters

ValueCountFrequency (%)
f 2770
18.2%
l 1273
 
8.3%
e 1273
 
8.3%
n 1071
 
7.0%
u 1058
 
6.9%
r 955
 
6.3%
a 886
 
5.8%
i 628
 
4.1%
y 628
 
4.1%
H 529
 
3.5%
Other values (11) 4185
27.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 13656
89.5%
Uppercase Letter 1600
 
10.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
f 2770
20.3%
l 1273
9.3%
e 1273
9.3%
n 1071
 
7.8%
u 1058
 
7.7%
r 955
 
7.0%
a 886
 
6.5%
i 628
 
4.6%
y 628
 
4.6%
p 529
 
3.9%
Other values (7) 2585
18.9%
Uppercase Letter
ValueCountFrequency (%)
H 529
33.1%
R 443
27.7%
G 327
20.4%
S 301
18.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 15256
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
f 2770
18.2%
l 1273
 
8.3%
e 1273
 
8.3%
n 1071
 
7.0%
u 1058
 
6.9%
r 955
 
6.3%
a 886
 
5.8%
i 628
 
4.1%
y 628
 
4.1%
H 529
 
3.5%
Other values (11) 4185
27.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15256
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
f 2770
18.2%
l 1273
 
8.3%
e 1273
 
8.3%
n 1071
 
7.0%
u 1058
 
6.9%
r 955
 
6.3%
a 886
 
5.8%
i 628
 
4.1%
y 628
 
4.1%
H 529
 
3.5%
Other values (11) 4185
27.4%

First Name
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct1215
Distinct (%)75.9%
Missing0
Missing (%)0.0%
Memory size12.6 KiB
Tiffany
 
6
Marty
 
5
Joseph
 
5
Laverne
 
5
Darwin
 
4
Other values (1210)
1575 

Length

Max length10
Median length9
Mean length5.84125
Min length2

Characters and Unicode

Total characters9346
Distinct characters51
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique913 ?
Unique (%)57.1%

Sample

1st rowTamara
2nd rowErich
3rd rowStephany
4th rowVesta
5th rowGaston

Common Values

ValueCountFrequency (%)
Tiffany 6
 
0.4%
Marty 5
 
0.3%
Joseph 5
 
0.3%
Laverne 5
 
0.3%
Darwin 4
 
0.2%
Johnnie 4
 
0.2%
Gena 4
 
0.2%
Rufus 4
 
0.2%
Fran 4
 
0.2%
Jessie 4
 
0.2%
Other values (1205) 1555
97.2%

Length

2023-01-27T16:58:33.360131image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tiffany 6
 
0.4%
joseph 5
 
0.3%
laverne 5
 
0.3%
marty 5
 
0.3%
jessie 4
 
0.2%
vickie 4
 
0.2%
wallace 4
 
0.2%
dennis 4
 
0.2%
leonard 4
 
0.2%
jung 4
 
0.2%
Other values (1205) 1555
97.2%

Most occurring characters

ValueCountFrequency (%)
a 1080
 
11.6%
e 1036
 
11.1%
n 779
 
8.3%
i 741
 
7.9%
r 672
 
7.2%
l 618
 
6.6%
o 495
 
5.3%
t 330
 
3.5%
s 293
 
3.1%
d 275
 
2.9%
Other values (41) 3027
32.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 7746
82.9%
Uppercase Letter 1600
 
17.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 1080
13.9%
e 1036
13.4%
n 779
10.1%
i 741
9.6%
r 672
8.7%
l 618
8.0%
o 495
 
6.4%
t 330
 
4.3%
s 293
 
3.8%
d 275
 
3.6%
Other values (16) 1427
18.4%
Uppercase Letter
ValueCountFrequency (%)
L 131
 
8.2%
C 126
 
7.9%
J 123
 
7.7%
A 122
 
7.6%
M 116
 
7.2%
S 115
 
7.2%
R 114
 
7.1%
D 106
 
6.6%
B 81
 
5.1%
E 74
 
4.6%
Other values (15) 492
30.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 9346
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 1080
 
11.6%
e 1036
 
11.1%
n 779
 
8.3%
i 741
 
7.9%
r 672
 
7.2%
l 618
 
6.6%
o 495
 
5.3%
t 330
 
3.5%
s 293
 
3.1%
d 275
 
2.9%
Other values (41) 3027
32.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9346
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 1080
 
11.6%
e 1036
 
11.1%
n 779
 
8.3%
i 741
 
7.9%
r 672
 
7.2%
l 618
 
6.6%
o 495
 
5.3%
t 330
 
3.5%
s 293
 
3.1%
d 275
 
2.9%
Other values (41) 3027
32.4%

Last Name
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct1378
Distinct (%)86.1%
Missing0
Missing (%)0.0%
Memory size12.6 KiB
Stewart
 
4
Maldonado
 
3
Allen
 
3
Champion
 
3
Sawyers
 
3
Other values (1373)
1584 

Length

Max length12
Median length10
Mean length6.2825
Min length2

Characters and Unicode

Total characters10052
Distinct characters51
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1175 ?
Unique (%)73.4%

Sample

1st rowHsu
2nd rowParedes
3rd rowBraun
4th rowMcmichael
5th rowGibbs

Common Values

ValueCountFrequency (%)
Stewart 4
 
0.2%
Maldonado 3
 
0.2%
Allen 3
 
0.2%
Champion 3
 
0.2%
Sawyers 3
 
0.2%
Bess 3
 
0.2%
Penny 3
 
0.2%
Day 3
 
0.2%
Hammonds 3
 
0.2%
Ashworth 3
 
0.2%
Other values (1368) 1569
98.1%

Length

2023-01-27T16:58:33.423904image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
stewart 4
 
0.2%
forrest 3
 
0.2%
maldonado 3
 
0.2%
braun 3
 
0.2%
leblanc 3
 
0.2%
crisp 3
 
0.2%
graham 3
 
0.2%
kersey 3
 
0.2%
easterling 3
 
0.2%
sharpe 3
 
0.2%
Other values (1368) 1569
98.1%

Most occurring characters

ValueCountFrequency (%)
e 1058
 
10.5%
a 835
 
8.3%
r 803
 
8.0%
n 768
 
7.6%
o 694
 
6.9%
l 676
 
6.7%
i 544
 
5.4%
s 443
 
4.4%
t 409
 
4.1%
d 296
 
2.9%
Other values (41) 3526
35.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8452
84.1%
Uppercase Letter 1600
 
15.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1058
12.5%
a 835
9.9%
r 803
9.5%
n 768
 
9.1%
o 694
 
8.2%
l 676
 
8.0%
i 544
 
6.4%
s 443
 
5.2%
t 409
 
4.8%
d 296
 
3.5%
Other values (16) 1926
22.8%
Uppercase Letter
ValueCountFrequency (%)
B 177
 
11.1%
S 159
 
9.9%
C 151
 
9.4%
M 148
 
9.2%
H 102
 
6.4%
L 89
 
5.6%
P 88
 
5.5%
G 79
 
4.9%
R 76
 
4.8%
D 75
 
4.7%
Other values (15) 456
28.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 10052
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1058
 
10.5%
a 835
 
8.3%
r 803
 
8.0%
n 768
 
7.6%
o 694
 
6.9%
l 676
 
6.7%
i 544
 
5.4%
s 443
 
4.4%
t 409
 
4.1%
d 296
 
2.9%
Other values (41) 3526
35.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10052
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1058
 
10.5%
a 835
 
8.3%
r 803
 
8.0%
n 768
 
7.6%
o 694
 
6.9%
l 676
 
6.7%
i 544
 
5.4%
s 443
 
4.4%
t 409
 
4.1%
d 296
 
2.9%
Other values (41) 3526
35.1%

Birthday
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct1065
Distinct (%)66.6%
Missing0
Missing (%)0.0%
Memory size12.6 KiB
1998-06-12
 
6
1998-05-10
 
5
1997-11-18
 
5
1998-09-30
 
5
1997-05-27
 
5
Other values (1060)
1574 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters16000
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique655 ?
Unique (%)40.9%

Sample

1st row2000-03-30
2nd row1999-10-14
3rd row1999-11-03
4th row2000-08-19
5th row1998-09-27

Common Values

ValueCountFrequency (%)
1998-06-12 6
 
0.4%
1998-05-10 5
 
0.3%
1997-11-18 5
 
0.3%
1998-09-30 5
 
0.3%
1997-05-27 5
 
0.3%
1997-05-13 5
 
0.3%
1999-07-29 4
 
0.2%
2001-04-04 4
 
0.2%
2000-06-30 4
 
0.2%
1997-12-02 4
 
0.2%
Other values (1055) 1553
97.1%

Length

2023-01-27T16:58:33.483467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
1998-06-12 6
 
0.4%
1997-11-18 5
 
0.3%
1998-09-30 5
 
0.3%
1997-05-27 5
 
0.3%
1997-05-13 5
 
0.3%
1998-05-10 5
 
0.3%
2001-05-08 4
 
0.2%
1998-01-12 4
 
0.2%
1998-11-05 4
 
0.2%
1997-06-20 4
 
0.2%
Other values (1055) 1553
97.1%

Most occurring characters

ValueCountFrequency (%)
0 3467
21.7%
- 3200
20.0%
9 2669
16.7%
1 2668
16.7%
2 1451
9.1%
7 645
 
4.0%
8 612
 
3.8%
3 391
 
2.4%
6 325
 
2.0%
4 293
 
1.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12800
80.0%
Dash Punctuation 3200
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3467
27.1%
9 2669
20.9%
1 2668
20.8%
2 1451
11.3%
7 645
 
5.0%
8 612
 
4.8%
3 391
 
3.1%
6 325
 
2.5%
4 293
 
2.3%
5 279
 
2.2%
Dash Punctuation
ValueCountFrequency (%)
- 3200
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16000
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3467
21.7%
- 3200
20.0%
9 2669
16.7%
1 2668
16.7%
2 1451
9.1%
7 645
 
4.0%
8 612
 
3.8%
3 391
 
2.4%
6 325
 
2.0%
4 293
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3467
21.7%
- 3200
20.0%
9 2669
16.7%
1 2668
16.7%
2 1451
9.1%
7 645
 
4.0%
8 612
 
3.8%
3 391
 
2.4%
6 325
 
2.0%
4 293
 
1.8%

Best Hand
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size12.6 KiB
Right
810 
Left
790 

Length

Max length5
Median length5
Mean length4.50625
Min length4

Characters and Unicode

Total characters7210
Distinct characters8
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLeft
2nd rowRight
3rd rowLeft
4th rowLeft
5th rowLeft

Common Values

ValueCountFrequency (%)
Right 810
50.6%
Left 790
49.4%

Length

2023-01-27T16:58:33.541103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-01-27T16:58:33.601227image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
right 810
50.6%
left 790
49.4%

Most occurring characters

ValueCountFrequency (%)
t 1600
22.2%
R 810
11.2%
i 810
11.2%
g 810
11.2%
h 810
11.2%
L 790
11.0%
e 790
11.0%
f 790
11.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5610
77.8%
Uppercase Letter 1600
 
22.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 1600
28.5%
i 810
14.4%
g 810
14.4%
h 810
14.4%
e 790
14.1%
f 790
14.1%
Uppercase Letter
ValueCountFrequency (%)
R 810
50.6%
L 790
49.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 7210
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 1600
22.2%
R 810
11.2%
i 810
11.2%
g 810
11.2%
h 810
11.2%
L 790
11.0%
e 790
11.0%
f 790
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 1600
22.2%
R 810
11.2%
i 810
11.2%
g 810
11.2%
h 810
11.2%
L 790
11.0%
e 790
11.0%
f 790
11.0%

Arithmancy
Real number (ℝ)

Distinct1540
Distinct (%)98.3%
Missing34
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean49634.57
Minimum-24370
Maximum104956
Zeros0
Zeros (%)0.0%
Negative5
Negative (%)0.3%
Memory size12.6 KiB
2023-01-27T16:58:33.896067image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-24370
5-th percentile23237.25
Q138511.5
median49013.5
Q360811.25
95-th percentile76606.5
Maximum104956
Range129326
Interquartile range (IQR)22299.75

Descriptive statistics

Standard deviation16679.806
Coefficient of variation (CV)0.33605219
Kurtosis0.26646825
Mean49634.57
Median Absolute Deviation (MAD)11272
Skewness-0.04195914
Sum77727737
Variance2.7821593 × 108
MonotonicityNot monotonic
2023-01-27T16:58:33.970296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47161 2
 
0.1%
52561 2
 
0.1%
44987 2
 
0.1%
63271 2
 
0.1%
47193 2
 
0.1%
37018 2
 
0.1%
38984 2
 
0.1%
54385 2
 
0.1%
44262 2
 
0.1%
57978 2
 
0.1%
Other values (1530) 1546
96.6%
(Missing) 34
 
2.1%
ValueCountFrequency (%)
-24370 1
0.1%
-13902 1
0.1%
-4491 1
0.1%
-3149 1
0.1%
-2464 1
0.1%
1775 1
0.1%
4047 1
0.1%
5929 1
0.1%
6407 1
0.1%
7899 1
0.1%
ValueCountFrequency (%)
104956 1
0.1%
101374 1
0.1%
99356 1
0.1%
98109 1
0.1%
98030 1
0.1%
96741 1
0.1%
93900 1
0.1%
93408 1
0.1%
93332 1
0.1%
91614 1
0.1%

Astronomy
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1568
Distinct (%)100.0%
Missing32
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean39.797131
Minimum-966.74055
Maximum1016.2119
Zeros0
Zeros (%)0.0%
Negative727
Negative (%)45.4%
Memory size12.6 KiB
2023-01-27T16:58:34.042216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-966.74055
5-th percentile-651.44835
Q1-489.55139
median260.28945
Q3524.77195
95-th percentile704.38828
Maximum1016.2119
Range1982.9525
Interquartile range (IQR)1014.3233

Descriptive statistics

Standard deviation520.29827
Coefficient of variation (CV)13.073763
Kurtosis-1.7103733
Mean39.797131
Median Absolute Deviation (MAD)455.67442
Skewness-0.09472534
Sum62401.901
Variance270710.29
MonotonicityNot monotonic
2023-01-27T16:58:34.115347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-487.886086 1
 
0.1%
739.6116098 1
 
0.1%
-495.9786636 1
 
0.1%
578.2199945 1
 
0.1%
-683.9674068 1
 
0.1%
714.6590801 1
 
0.1%
849.1318778 1
 
0.1%
640.8140111 1
 
0.1%
327.9125957 1
 
0.1%
-84.43963221 1
 
0.1%
Other values (1558) 1558
97.4%
(Missing) 32
 
2.0%
ValueCountFrequency (%)
-966.7405456 1
0.1%
-834.4753352 1
0.1%
-817.9170271 1
0.1%
-799.9813303 1
0.1%
-792.4373384 1
0.1%
-790.6285204 1
0.1%
-790.0267716 1
0.1%
-777.5890783 1
0.1%
-766.519901 1
0.1%
-765.1109488 1
0.1%
ValueCountFrequency (%)
1016.21194 1
0.1%
970.6176788 1
0.1%
956.4844384 1
0.1%
940.3980794 1
0.1%
873.4851393 1
0.1%
870.0722355 1
0.1%
860.8581858 1
0.1%
849.1318778 1
0.1%
842.5382285 1
0.1%
825.5019412 1
0.1%

Herbology
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1567
Distinct (%)100.0%
Missing33
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean1.1410195
Minimum-10.295663
Maximum11.612895
Zeros0
Zeros (%)0.0%
Negative603
Negative (%)37.7%
Memory size12.6 KiB
2023-01-27T16:58:34.188579image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-10.295663
5-th percentile-6.9959715
Q1-4.3081818
median3.4690121
Q35.4191835
95-th percentile7.4737605
Maximum11.612895
Range21.908558
Interquartile range (IQR)9.7273653

Descriptive statistics

Standard deviation5.219682
Coefficient of variation (CV)4.5745773
Kurtosis-1.3905247
Mean1.1410195
Median Absolute Deviation (MAD)3.112178
Skewness-0.39876117
Sum1787.9776
Variance27.24508
MonotonicityNot monotonic
2023-01-27T16:58:34.258599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.727180299 1
 
0.1%
-0.4183538039 1
 
0.1%
4.47666341 1
 
0.1%
-2.668746722 1
 
0.1%
-7.775641491 1
 
0.1%
3.993271168 1
 
0.1%
4.615926843 1
 
0.1%
-6.484531499 1
 
0.1%
5.696834328 1
 
0.1%
2.929898031 1
 
0.1%
Other values (1557) 1557
97.3%
(Missing) 33
 
2.1%
ValueCountFrequency (%)
-10.29566275 1
0.1%
-10.08358184 1
0.1%
-9.953412078 1
0.1%
-9.57392446 1
0.1%
-9.563072322 1
0.1%
-9.142445007 1
0.1%
-8.930238481 1
0.1%
-8.887563696 1
0.1%
-8.847165627 1
0.1%
-8.713620342 1
0.1%
ValueCountFrequency (%)
11.61289508 1
0.1%
10.29675934 1
0.1%
10.15399255 1
0.1%
9.942055511 1
0.1%
9.489892166 1
0.1%
9.460452149 1
0.1%
9.441353043 1
0.1%
9.214189385 1
0.1%
9.132216167 1
0.1%
9.069076806 1
0.1%

Defense Against the Dark Arts
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1569
Distinct (%)100.0%
Missing31
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean-0.3878635
Minimum-10.162119
Maximum9.6674055
Zeros0
Zeros (%)0.0%
Negative840
Negative (%)52.5%
Memory size12.6 KiB
2023-01-27T16:58:34.331722image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-10.162119
5-th percentile-7.0467707
Q1-5.2590954
median-2.5893416
Q34.9046801
95-th percentile6.540417
Maximum9.6674055
Range19.829525
Interquartile range (IQR)10.163775

Descriptive statistics

Standard deviation5.2127937
Coefficient of variation (CV)-13.439764
Kurtosis-1.7105965
Mean-0.3878635
Median Absolute Deviation (MAD)4.585098
Skewness0.093346826
Sum-608.55783
Variance27.173218
MonotonicityNot monotonic
2023-01-27T16:58:34.406130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.87886086 1
 
0.1%
4.948433339 1
 
0.1%
-4.622829706 1
 
0.1%
4.959786636 1
 
0.1%
-5.782199945 1
 
0.1%
-7.146590801 1
 
0.1%
-8.491318778 1
 
0.1%
-3.279125957 1
 
0.1%
0.8443963221 1
 
0.1%
4.489114745 1
 
0.1%
Other values (1559) 1559
97.4%
(Missing) 31
 
1.9%
ValueCountFrequency (%)
-10.1621194 1
0.1%
-9.706176788 1
0.1%
-9.564844384 1
0.1%
-9.403980794 1
0.1%
-8.734851393 1
0.1%
-8.700722355 1
0.1%
-8.674625356 1
0.1%
-8.608581858 1
0.1%
-8.491318778 1
0.1%
-8.425382285 1
0.1%
ValueCountFrequency (%)
9.667405456 1
0.1%
8.344753352 1
0.1%
8.179170271 1
0.1%
7.999813303 1
0.1%
7.924373384 1
0.1%
7.906285204 1
0.1%
7.900267716 1
0.1%
7.840030898 1
0.1%
7.775890783 1
0.1%
7.66519901 1
0.1%

Divination
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1421
Distinct (%)91.0%
Missing39
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean3.1539097
Minimum-8.727
Maximum10.032
Zeros0
Zeros (%)0.0%
Negative286
Negative (%)17.9%
Memory size12.6 KiB
2023-01-27T16:58:34.478057image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-8.727
5-th percentile-6.043
Q13.099
median4.624
Q35.667
95-th percentile7.091
Maximum10.032
Range18.759
Interquartile range (IQR)2.568

Descriptive statistics

Standard deviation4.1553009
Coefficient of variation (CV)1.3175079
Kurtosis0.58052884
Mean3.1539097
Median Absolute Deviation (MAD)1.224
Skewness-1.3805247
Sum4923.253
Variance17.266526
MonotonicityNot monotonic
2023-01-27T16:58:34.553276image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.437 4
 
0.2%
5.445 3
 
0.2%
5.142 3
 
0.2%
5.254 3
 
0.2%
5.759 3
 
0.2%
-6.254 3
 
0.2%
5.236 3
 
0.2%
6.223 3
 
0.2%
5.574 3
 
0.2%
4.515 3
 
0.2%
Other values (1411) 1530
95.6%
(Missing) 39
 
2.4%
ValueCountFrequency (%)
-8.727 1
0.1%
-8.724 1
0.1%
-8.041 1
0.1%
-8.031 1
0.1%
-7.964 1
0.1%
-7.952 1
0.1%
-7.789 1
0.1%
-7.644 1
0.1%
-7.552 1
0.1%
-7.491 1
0.1%
ValueCountFrequency (%)
10.032 1
0.1%
9.421 1
0.1%
9.282 1
0.1%
9.209 1
0.1%
8.94 1
0.1%
8.893 1
0.1%
8.86 1
0.1%
8.644 1
0.1%
8.583 1
0.1%
8.561 1
0.1%

Muggle Studies
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1565
Distinct (%)100.0%
Missing35
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean-224.58991
Minimum-1086.4968
Maximum1092.3886
Zeros0
Zeros (%)0.0%
Negative1129
Negative (%)70.6%
Memory size12.6 KiB
2023-01-27T16:58:34.626510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-1086.4968
5-th percentile-765.28112
Q1-577.5801
median-419.16429
Q3254.99486
95-th percentile668.50803
Maximum1092.3886
Range2178.8854
Interquartile range (IQR)832.57495

Descriptive statistics

Standard deviation486.34484
Coefficient of variation (CV)-2.1654794
Kurtosis-0.71912562
Mean-224.58991
Median Absolute Deviation (MAD)196.95253
Skewness0.8256152
Sum-351483.22
Variance236531.3
MonotonicityNot monotonic
2023-01-27T16:58:34.698987image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-515.2669526 1
 
0.1%
-387.3165899 1
 
0.1%
-558.6880173 1
 
0.1%
-524.394691 1
 
0.1%
363.5733232 1
 
0.1%
-518.1704389 1
 
0.1%
-442.0018219 1
 
0.1%
-101.8394605 1
 
0.1%
-205.7193158 1
 
0.1%
411.8083803 1
 
0.1%
Other values (1555) 1555
97.2%
(Missing) 35
 
2.2%
ValueCountFrequency (%)
-1086.496835 1
0.1%
-1043.961527 1
0.1%
-1040.739017 1
0.1%
-982.6816811 1
0.1%
-974.042878 1
0.1%
-967.3473614 1
0.1%
-962.4521458 1
0.1%
-944.5888987 1
0.1%
-935.6573541 1
0.1%
-928.1155974 1
0.1%
ValueCountFrequency (%)
1092.388611 1
0.1%
1006.103921 1
0.1%
949.495717 1
0.1%
945.3136328 1
0.1%
932.4176117 1
0.1%
930.2439369 1
0.1%
928.7153976 1
0.1%
928.3178728 1
0.1%
908.5604666 1
0.1%
887.0278524 1
0.1%

Ancient Runes
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1565
Distinct (%)100.0%
Missing35
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean495.74797
Minimum283.86961
Maximum745.39622
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-01-27T16:58:34.767980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum283.86961
5-th percentile351.05621
Q1397.51105
median463.91831
Q3597.49223
95-th percentile639.99714
Maximum745.39622
Range461.52661
Interquartile range (IQR)199.98118

Descriptive statistics

Standard deviation106.28516
Coefficient of variation (CV)0.21439354
Kurtosis-1.5911095
Mean495.74797
Median Absolute Deviation (MAD)100.22086
Skewness0.033548989
Sum775845.57
Variance11296.536
MonotonicityNot monotonic
2023-01-27T16:58:34.840346image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
532.4842261 1
 
0.1%
597.3297442 1
 
0.1%
328.5446156 1
 
0.1%
627.6412132 1
 
0.1%
398.4905964 1
 
0.1%
689.6572058 1
 
0.1%
312.7376724 1
 
0.1%
347.4120254 1
 
0.1%
611.9905756 1
 
0.1%
376.3130704 1
 
0.1%
Other values (1555) 1555
97.2%
(Missing) 35
 
2.2%
ValueCountFrequency (%)
283.8696087 1
0.1%
298.2189599 1
0.1%
299.9832769 1
0.1%
302.562409 1
0.1%
302.8929088 1
0.1%
307.8595363 1
0.1%
311.9260923 1
0.1%
312.7376724 1
0.1%
312.8282723 1
0.1%
314.3012235 1
0.1%
ValueCountFrequency (%)
745.3962199 1
0.1%
696.6504423 1
0.1%
690.8804539 1
0.1%
689.6814846 1
0.1%
689.6572058 1
0.1%
680.1641353 1
0.1%
679.2091877 1
0.1%
676.6492791 1
0.1%
675.9053382 1
0.1%
675.6636353 1
0.1%

History of Magic
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1557
Distinct (%)100.0%
Missing43
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean2.9630946
Minimum-8.858993
Maximum11.889713
Zeros0
Zeros (%)0.0%
Negative320
Negative (%)20.0%
Memory size12.6 KiB
2023-01-27T16:58:34.909324image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-8.858993
5-th percentile-6.0678418
Q12.2186533
median4.3781755
Q35.8252417
95-th percentile7.8380858
Maximum11.889713
Range20.748706
Interquartile range (IQR)3.6065883

Descriptive statistics

Standard deviation4.4257747
Coefficient of variation (CV)1.4936326
Kurtosis-0.06365675
Mean2.9630946
Median Absolute Deviation (MAD)1.6196611
Skewness-1.0478591
Sum4613.5383
Variance19.587481
MonotonicityNot monotonic
2023-01-27T16:58:34.979307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.231058287 1
 
0.1%
-7.429914877 1
 
0.1%
4.914088801 1
 
0.1%
6.826083337 1
 
0.1%
4.096357976 1
 
0.1%
6.348792415 1
 
0.1%
5.244343733 1
 
0.1%
6.066305322 1
 
0.1%
5.19238692 1
 
0.1%
9.823889037 1
 
0.1%
Other values (1547) 1547
96.7%
(Missing) 43
 
2.7%
ValueCountFrequency (%)
-8.858992992 1
0.1%
-8.431116673 1
0.1%
-8.346356422 1
0.1%
-7.909154308 1
0.1%
-7.868142329 1
0.1%
-7.758135771 1
0.1%
-7.712050784 1
0.1%
-7.603293485 1
0.1%
-7.578273366 1
0.1%
-7.545057581 1
0.1%
ValueCountFrequency (%)
11.88971275 1
0.1%
11.47936914 1
0.1%
10.87046471 1
0.1%
10.7984911 1
0.1%
10.69302455 1
0.1%
10.68904577 1
0.1%
10.31282069 1
0.1%
10.16575617 1
0.1%
9.997830217 1
0.1%
9.98695849 1
0.1%

Transfiguration
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1566
Distinct (%)100.0%
Missing34
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean1030.0969
Minimum906.62732
Maximum1098.9582
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.6 KiB
2023-01-27T16:58:35.049785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum906.62732
5-th percentile937.70199
Q11026.21
median1045.507
Q31058.4364
95-th percentile1075.4762
Maximum1098.9582
Range192.33088
Interquartile range (IQR)32.226418

Descriptive statistics

Standard deviation44.125116
Coefficient of variation (CV)0.042835886
Kurtosis0.23407124
Mean1030.0969
Median Absolute Deviation (MAD)14.443275
Skewness-1.2303434
Sum1613131.8
Variance1947.0259
MonotonicityNot monotonic
2023-01-27T16:58:35.124912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1039.788281 1
 
0.1%
1028.800461 1
 
0.1%
1046.051087 1
 
0.1%
1068.857723 1
 
0.1%
1047.18369 1
 
0.1%
1059.628068 1
 
0.1%
1051.868959 1
 
0.1%
1053.751232 1
 
0.1%
1046.453536 1
 
0.1%
1051.898621 1
 
0.1%
Other values (1556) 1556
97.2%
(Missing) 34
 
2.1%
ValueCountFrequency (%)
906.6273197 1
0.1%
907.190274 1
0.1%
909.1788845 1
0.1%
910.3387077 1
0.1%
910.3927568 1
0.1%
910.7900881 1
0.1%
911.0984849 1
0.1%
912.5344286 1
0.1%
915.0064299 1
0.1%
915.1098511 1
0.1%
ValueCountFrequency (%)
1098.958201 1
0.1%
1094.460614 1
0.1%
1093.309767 1
0.1%
1092.888016 1
0.1%
1091.054831 1
0.1%
1090.490143 1
0.1%
1090.29017 1
0.1%
1090.201068 1
0.1%
1090.06535 1
0.1%
1089.567004 1
0.1%

Potions
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1570
Distinct (%)100.0%
Missing30
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean5.950373
Minimum-4.6974838
Maximum13.536762
Zeros0
Zeros (%)0.0%
Negative39
Negative (%)2.4%
Memory size12.6 KiB
2023-01-27T16:58:35.198236image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-4.6974838
5-th percentile1.050341
Q13.646785
median5.8748373
Q38.2481725
95-th percentile11.041538
Maximum13.536762
Range18.234246
Interquartile range (IQR)4.6013875

Descriptive statistics

Standard deviation3.1478543
Coefficient of variation (CV)0.52901797
Kurtosis-0.51679423
Mean5.950373
Median Absolute Deviation (MAD)2.3312833
Skewness-0.024458891
Sum9342.0856
Variance9.9089864
MonotonicityNot monotonic
2023-01-27T16:58:35.272523image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.790369066 1
 
0.1%
2.295259466 1
 
0.1%
3.276067146 1
 
0.1%
-0.1245919409 1
 
0.1%
-0.1292481814 1
 
0.1%
9.783696767 1
 
0.1%
6.891703506 1
 
0.1%
2.782168551 1
 
0.1%
4.839887577 1
 
0.1%
8.153764068 1
 
0.1%
Other values (1560) 1560
97.5%
(Missing) 30
 
1.9%
ValueCountFrequency (%)
-4.697483768 1
0.1%
-3.620761628 1
0.1%
-2.668485263 1
0.1%
-2.422484663 1
0.1%
-1.815418746 1
0.1%
-1.703318598 1
0.1%
-1.458926104 1
0.1%
-1.415213807 1
0.1%
-1.404883713 1
0.1%
-1.372314655 1
0.1%
ValueCountFrequency (%)
13.53676212 1
0.1%
13.48141807 1
0.1%
13.41525375 1
0.1%
13.41317384 1
0.1%
13.35871983 1
0.1%
13.35055394 1
0.1%
13.33790237 1
0.1%
13.29442685 1
0.1%
13.2405574 1
0.1%
13.17291327 1
0.1%

Care of Magical Creatures
Real number (ℝ)

Distinct1560
Distinct (%)100.0%
Missing40
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean-0.053427137
Minimum-3.3136758
Maximum3.0565458
Zeros0
Zeros (%)0.0%
Negative803
Negative (%)50.2%
Memory size12.6 KiB
2023-01-27T16:58:35.343251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-3.3136758
5-th percentile-1.7151757
Q1-0.67160647
median-0.04481105
Q30.58991935
95-th percentile1.5234027
Maximum3.0565458
Range6.3702215
Interquartile range (IQR)1.2615258

Descriptive statistics

Standard deviation0.97145697
Coefficient of variation (CV)-18.182838
Kurtosis0.010774069
Mean-0.053427137
Median Absolute Deviation (MAD)0.63247088
Skewness-0.061668963
Sum-83.346333
Variance0.94372864
MonotonicityNot monotonic
2023-01-27T16:58:35.414232image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.715939127 1
 
0.1%
-0.07992088879 1
 
0.1%
-1.278591379 1
 
0.1%
0.3819658703 1
 
0.1%
-0.1287047247 1
 
0.1%
-0.3898613717 1
 
0.1%
1.355436881 1
 
0.1%
-2.387181485 1
 
0.1%
0.7411716489 1
 
0.1%
-0.2658345897 1
 
0.1%
Other values (1550) 1550
96.9%
(Missing) 40
 
2.5%
ValueCountFrequency (%)
-3.313675764 1
0.1%
-2.973104147 1
0.1%
-2.95315194 1
0.1%
-2.880505308 1
0.1%
-2.665338302 1
0.1%
-2.656562374 1
0.1%
-2.653451252 1
0.1%
-2.645500714 1
0.1%
-2.599141661 1
0.1%
-2.524625044 1
0.1%
ValueCountFrequency (%)
3.056545774 1
0.1%
2.948045494 1
0.1%
2.782511946 1
0.1%
2.723857755 1
0.1%
2.512530215 1
0.1%
2.496775229 1
0.1%
2.445931566 1
0.1%
2.42918854 1
0.1%
2.416330036 1
0.1%
2.378512003 1
0.1%

Charms
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1600
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-243.37441
Minimum-261.04892
Maximum-225.42814
Zeros0
Zeros (%)0.0%
Negative1600
Negative (%)100.0%
Memory size12.6 KiB
2023-01-27T16:58:35.484603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-261.04892
5-th percentile-255.15823
Q1-250.6526
median-244.86777
Q3-232.5523
95-th percentile-229.35325
Maximum-225.42814
Range35.62078
Interquartile range (IQR)18.100295

Descriptive statistics

Standard deviation8.7836399
Coefficient of variation (CV)-0.036091058
Kurtosis-1.0858818
Mean-243.37441
Median Absolute Deviation (MAD)6.380905
Skewness0.39074454
Sum-389399.05
Variance77.152329
MonotonicityNot monotonic
2023-01-27T16:58:35.560996image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-232.79405 1
 
0.1%
-254.661 1
 
0.1%
-245.05749 1
 
0.1%
-244.63686 1
 
0.1%
-251.92038 1
 
0.1%
-244.65275 1
 
0.1%
-230.67934 1
 
0.1%
-244.6005 1
 
0.1%
-251.91111 1
 
0.1%
-245.78281 1
 
0.1%
Other values (1590) 1590
99.4%
ValueCountFrequency (%)
-261.04892 1
0.1%
-260.50346 1
0.1%
-260.40861 1
0.1%
-259.51864 1
0.1%
-259.49964 1
0.1%
-259.18162 1
0.1%
-259.1363 1
0.1%
-258.97753 1
0.1%
-258.83154 1
0.1%
-258.72942 1
0.1%
ValueCountFrequency (%)
-225.42814 1
0.1%
-226.12874 1
0.1%
-226.76896 1
0.1%
-227.02916 1
0.1%
-227.2289 1
0.1%
-227.25197 1
0.1%
-227.33781 1
0.1%
-227.34265 1
0.1%
-227.4041 1
0.1%
-227.47385 1
0.1%

Flying
Real number (ℝ)

Distinct1546
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.958012
Minimum-181.47
Maximum279.07
Zeros0
Zeros (%)0.0%
Negative837
Negative (%)52.3%
Memory size12.6 KiB
2023-01-27T16:58:35.636635image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-181.47
5-th percentile-99.1985
Q1-41.87
median-2.515
Q350.56
95-th percentile218.321
Maximum279.07
Range460.54
Interquartile range (IQR)92.43

Descriptive statistics

Standard deviation97.631602
Coefficient of variation (CV)4.4462859
Kurtosis-0.14446624
Mean21.958012
Median Absolute Deviation (MAD)44.125
Skewness0.88415428
Sum35132.82
Variance9531.9297
MonotonicityNot monotonic
2023-01-27T16:58:35.716905image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-57.27 3
 
0.2%
-43.78 3
 
0.2%
4.68 2
 
0.1%
-34.52 2
 
0.1%
223.69 2
 
0.1%
2.5 2
 
0.1%
30 2
 
0.1%
1.54 2
 
0.1%
-58.56 2
 
0.1%
-9.64 2
 
0.1%
Other values (1536) 1578
98.6%
ValueCountFrequency (%)
-181.47 1
0.1%
-180.37 1
0.1%
-176.72 1
0.1%
-162.55 1
0.1%
-162.04 1
0.1%
-158.91 1
0.1%
-152.9 1
0.1%
-145.59 1
0.1%
-140.63 1
0.1%
-140.12 1
0.1%
ValueCountFrequency (%)
279.07 1
0.1%
275.72 1
0.1%
273.96 1
0.1%
270.27 1
0.1%
263.53 1
0.1%
263.5 1
0.1%
261.55 1
0.1%
260.78 1
0.1%
260.75 1
0.1%
257.42 1
0.1%

Interactions

2023-01-27T16:58:31.642453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:17.461504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:18.540705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:19.585429image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:20.688826image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:21.703621image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:22.912730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:23.992723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:25.020695image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:26.188226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:27.210550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:28.294371image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:29.320144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:30.571113image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:31.718570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:17.533543image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:18.610991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:19.660213image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:20.758600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:21.794394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:22.989676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:24.062307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:25.092415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:26.262494image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:27.285980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:28.370500image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:29.582016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:30.645278image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:31.790608image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:17.601889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:18.679627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:19.732584image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:20.825730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:21.869796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:23.079986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:24.134482image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:25.162359image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:26.338309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:27.361599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:28.441524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:29.656062image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:30.716280image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:31.862868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:17.672060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:18.751672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:19.806120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:20.898308image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:21.966101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:23.152852image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:24.205506image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:25.232403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:26.423450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:27.449776image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:28.516006image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:29.744845image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:30.791684image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:31.930706image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:17.739320image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:18.836600image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:19.876837image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:20.973171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:22.036488image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:23.224415image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:24.271856image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:25.298988image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:26.494400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:27.519908image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:28.586746image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:29.817958image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:30.861551image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:32.004041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:17.817162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:18.908442image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:19.951824image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:21.042593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:22.110893image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:23.304999image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:24.350145image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:25.382292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:26.565347image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:27.595142image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:28.661767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:29.915400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:30.941699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:32.076923image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:17.907297image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:18.980266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:20.024026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:21.118237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:22.183418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:23.389343image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:24.423758image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:25.468190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:26.635000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:27.678044image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:28.734642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:29.988874image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:31.022252image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:32.145156image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:17.975846image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:19.052116image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:20.092037image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:21.202679image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:22.254222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:23.461099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:24.494254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:25.535032image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:26.703051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:27.767937image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:28.804537image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:30.062798image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:31.104634image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:32.218035image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:18.041991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:19.130299image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:20.163866image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:21.267881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:22.324286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:23.533611image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:24.565726image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:25.604752image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:26.774812image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:27.844072image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:28.878962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:30.134471image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:31.195175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:32.286556image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:18.109763image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:19.198026image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:20.230803image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:21.337727image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:22.393175image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:23.619021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:24.648017image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:25.678089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:26.850619image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:27.914244image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:28.949042image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:30.203099image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:31.275448image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:32.363806image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:18.181133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:19.274627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:20.396900image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:21.408046image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:22.467117image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:23.706325image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:24.731925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:25.903519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:26.926569image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:27.988913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:29.024318image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:30.279314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:31.349839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:32.436043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:18.257016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:19.346745image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:20.468639image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:21.478293image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:22.545060image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:23.777118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:24.805078image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:25.973162image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:27.004825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:28.062486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:29.098588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:30.353047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:31.424581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:32.507787image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:18.325192image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:19.419274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:20.541221image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:21.548931image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:22.632337image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:23.849486image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:24.876669image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:26.044458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:27.072825image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:28.134807image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:29.170666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:30.424699image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:31.498220image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:32.579510image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:18.469303image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:19.508656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:20.615919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:21.632925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:22.826564image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:23.921189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:24.946681image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:26.117657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:27.142882image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:28.211458image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:29.244394image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:30.497642image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2023-01-27T16:58:31.570779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2023-01-27T16:58:35.789939image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
IndexArithmancyAstronomyHerbologyDefense Against the Dark ArtsDivinationMuggle StudiesAncient RunesHistory of MagicTransfigurationPotionsCare of Magical CreaturesCharmsFlyingHogwarts HouseBest Hand
Index1.0000.053-0.0510.0060.048-0.0370.0180.0210.0400.0030.024-0.0020.020-0.0220.0000.000
Arithmancy0.0531.000-0.084-0.0180.0870.047-0.1000.129-0.0820.028-0.275-0.054-0.107-0.2710.0690.017
Astronomy-0.051-0.0841.0000.018-1.0000.393-0.374-0.138-0.292-0.339-0.4860.013-0.3930.4800.5660.012
Herbology0.006-0.0180.0181.000-0.0210.3370.342-0.0340.2890.269-0.1480.0440.7670.0370.5550.074
Defense Against the Dark Arts0.0480.087-1.000-0.0211.000-0.3920.3710.1380.2920.3410.482-0.0150.395-0.4840.5650.040
Divination-0.0370.0470.3930.337-0.3921.0000.1250.376-0.0400.046-0.2870.0320.3870.2670.5620.063
Muggle Studies0.018-0.100-0.3740.3420.3710.1251.0000.4250.2080.0130.1480.0470.618-0.0410.5610.000
Ancient Runes0.0210.129-0.138-0.0340.1380.3760.4251.000-0.356-0.320-0.067-0.0030.2180.5150.5610.041
History of Magic0.040-0.082-0.2920.2890.292-0.0400.208-0.3561.0000.4570.5850.0020.516-0.7010.5700.011
Transfiguration0.0030.028-0.3390.2690.3410.0460.013-0.3200.4571.0000.5370.0160.445-0.6370.5600.000
Potions0.024-0.275-0.486-0.1480.482-0.2870.148-0.0670.5850.5371.0000.0000.250-0.5170.4510.029
Care of Magical Creatures-0.002-0.0540.0130.044-0.0150.0320.047-0.0030.0020.0160.0001.0000.0460.0070.0200.000
Charms0.020-0.107-0.3930.7670.3950.3870.6180.2180.5160.4450.2500.0461.000-0.1900.7710.000
Flying-0.022-0.2710.4800.037-0.4840.267-0.0410.515-0.701-0.637-0.5170.007-0.1901.0000.6590.000
Hogwarts House0.0000.0690.5660.5550.5650.5620.5610.5610.5700.5600.4510.0200.7710.6591.0000.000
Best Hand0.0000.0170.0120.0740.0400.0630.0000.0410.0110.0000.0290.0000.0000.0000.0001.000

Missing values

2023-01-27T16:58:32.689171image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-01-27T16:58:32.799207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-01-27T16:58:32.932743image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

IndexHogwarts HouseFirst NameLast NameBirthdayBest HandArithmancyAstronomyHerbologyDefense Against the Dark ArtsDivinationMuggle StudiesAncient RunesHistory of MagicTransfigurationPotionsCare of Magical CreaturesCharmsFlying
00RavenclawTamaraHsu2000-03-30Left58384.0-487.8860865.7271804.8788614.722272.035831532.4842265.2310581039.7882813.7903690.715939-232.79405-26.89
11SlytherinErichParedes1999-10-14Right67239.0-552.060507-5.9874465.520605-5.612-487.340557367.7603034.1071701058.9445927.2487420.091674-252.18425-113.45
22RavenclawStephanyBraun1999-11-03Left23702.0-366.0761177.7250173.6607616.140664.893521602.5852843.5555791088.0883488.728531-0.515327-227.3426530.42
33GryffindorVestaMcmichael2000-08-19Left32667.0697.742809-6.497214-6.9774284.026-537.001128523.982133-4.809637920.3914490.821911-0.014040-256.84675200.64
44GryffindorGastonGibbs1998-09-27Left60158.0436.775204-7.820623NaN2.236-444.262537599.324514-3.444377937.4347244.311066-0.264070-256.38730157.98
55SlytherinCorrineHammond1999-04-04Right21209.0-613.687160-4.2891976.136872-6.592-440.997704396.2018045.3802861052.84516411.7512121.049894-247.94549-34.69
66GryffindorTomGuido2000-09-30Left49167.0628.046051-4.861976-6.280461NaN-926.892512583.742442-7.322486923.5395731.6466660.153022-257.83447261.55
77HufflepuffAliciaHayward1997-07-08Right33010.0411.4127275.931832-4.1141272.769-502.021336439.351416NaN1041.0919356.581791-0.171704-244.0349272.25
88GryffindorBellaLeatherman1998-12-07Left20278.0496.394945-5.215891-4.9639495.855-626.552041567.842402-6.198661925.2555001.0865181.147032-252.27561244.11
99HufflepuffHaydenAponte2001-10-13Right46316.0527.1935857.922205-5.2719363.356-398.101991341.4756064.9786141041.4146652.068824-0.529579-244.57527-0.09
IndexHogwarts HouseFirst NameLast NameBirthdayBest HandArithmancyAstronomyHerbologyDefense Against the Dark ArtsDivinationMuggle StudiesAncient RunesHistory of MagicTransfigurationPotionsCare of Magical CreaturesCharmsFlying
15901590RavenclawChadwickHawkins1996-11-21Right28744.0-569.5133806.9153285.6951343.958559.775978543.4311802.8820751065.5265245.038939-1.268810-229.8486017.86
15911591SlytherinDarrelGleason1998-09-07Right63538.0-507.715746-4.9976105.077157-5.637-443.781855386.0585136.9906031019.5264537.6962680.049255-251.06254-94.84
15921592GryffindorAdellDodge1998-05-17Right60006.0376.920722-2.949527-3.7692077.311-416.672294624.056215-6.638366954.799304-0.1053040.089692-249.35589193.78
15931593RavenclawAdelineChurch1998-01-22Left79653.0-426.1754015.6811074.2617546.205473.879478647.2388096.2542271046.8156277.2061560.326725-230.80139-29.82
15941594HufflepuffJeanieMora1999-08-27Left22927.0599.9016125.479485-5.9990165.543-525.883264467.9504185.9332111034.3944289.3440541.422887-241.9694071.23
15951595GryffindorJungBlank2001-09-14Right49009.0354.280086-4.541837-3.5428015.702-497.235066618.220213-5.231721964.2198533.389086-0.649983-250.39401185.83
15961596SlytherinShelliLock1998-03-12Left63296.0367.5311746.061064-3.6753121.757-643.271092445.8275652.2381121056.1473665.825263-0.333962-246.4271944.80
15971597GryffindorBenjaminChristensen1999-10-24Right63905.0544.018925-3.203269-5.4401896.065-385.150457635.211486-5.984257953.8666851.7098080.071569-251.63679198.47
15981598HufflepuffCharlotteDillon2001-09-21Left82713.0453.6762193.442831-4.5367626.738-831.741123383.4449373.8131111087.9492053.904100-0.531875-246.19072-76.81
15991599HufflepuffKylieNowak2000-08-21Left48639.0688.9119895.421046-6.8891206.593-234.207911339.7751547.2084151034.9280042.0522150.150532-244.02063-54.77